Falls are a significant cause of morbidity and mortality in the elderly. A robust and low-cost solution for the estimation of fall risk and detection of falls will allow seniors to live independently and reduce medical costs due to falls. Wearable devices have been developed to detect “hard falls”, namely falls that cause injury. However, many falls in the elderly do not cause physical injury (“soft falls”). These occur in association with weight transfer activities such as turning and sit-stand transitions. Indeed, the ability to control the position and movements of the trunk (“core”) is essential for coordinating the movements of the limbs during weight transfer. The goal of this project is to combine real-world limb-core dynamics of an individual with data collected by accelerometer via a commodity wristwatch and a cell phone on the opposite hip to improve the detection of hard and soft falls. A personalized fall risk analysis and detection model will be created for each user via real-time learning of the limb-core dynamics using state of the art machine learning algorithm. We will also assess the perceptions and preferences of elderly patients using this technology and evaluate their attitudes towards continuous data collection and sharing of health data for improved health. The software system, the real-world gait and weight transfer movement and the associated accelerometer data will be made freely available to any institution, investigator or research student interested in the study of machine learning on health conditions as well as on fall risk and analysis. This project will train graduate and undergraduate students in technical skills (machine learning, wearable technologies and data analysis skills) as well as in people skills for working with the elderly who live in long term care facilities.
10-2021: One Crossmodal KD paper has been submitted to ICASSP 2022.
11-2021: Funded Ph.D. Positions available
01-2022: UPDATE: Crossmodal KD paper has been accepted to ICASSP 2022.
04-2022: One Progressive KD paper has been submitted to ACM MM 2022.
07-2022: UPDATE: Progressive KD paper has been accepted to ACM MM 2022.
Progressive Cross-modal Knowledge Distillation for Human Action Recognition
Ni, J., Ngu, A. H., & Yan, Y.
To appear in ACM MM 2022.
TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network
Li, X., Metsis, V., Wang, H., & Ngu, A. H. H.
To appear in AIME 2022, Canada.
Cross-modal knowledge distillation for Vision-to-Sensor action recognition
Ni, J., Sarbajna, R., Liu, Y., Ngu, A. H., & Yan, Y.
ICASSP 2022,pp. 4448-4452
Collaborative Edge-Cloud Computing for Personalized Fall Detection
Ngu, A. H., Coyne, S., Srinivas, P., & Metsis, V.
AIAI 2021,pp. 323-336
SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning
Mauldin, T. R., Canby, M. E., Metsis, V., Ngu, A. H., & Rivera, C. C.
Sensors, 18(10), 3363.
Ensemble Deep Learning on Wearables Using Small Datasets
Mauldin, T., Ngu, A. H., Metsis, V., & Canby, M. E.
ACM Transactions on Computing for Healthcare, 2(1), 1-30.